Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Signed Neuron with Memory: Towards Simple, Accurate and High-Efficient ANN-SNN Conversion
Authors: Yuchen Wang, Malu Zhang, Yi Chen, Hong Qu
IJCAI 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We conduct experiments on challenging datasets including CIFAR10 (95.44% top-1), CIFAR100 (78.3% top-1) and Image Net (73.16% top-1). Experimental results demonstrate that the proposed method outperforms the state-of-the-art works in terms of accuracy and inference time. |
| Researcher Affiliation | Academia | Yuchen Wang , Malu Zhang , Yi Chen and Hong Qu School of Computer Science and Engineering, University of Electronic Science and Technology of China EMAIL, EMAIL, EMAIL, EMAIL |
| Pseudocode | No | The paper describes mathematical equations for the neuron model (Eq. 7-11) but does not contain structured pseudocode or algorithm blocks (clearly labeled algorithm sections or code-like formatted procedures). |
| Open Source Code | Yes | The code is available at https://github.com/ppppps/ ANN2SNNConversion SNM Neuron Norm. |
| Open Datasets | Yes | We use VGG and Res Net network structures to conduct experiments on CIFAR10, CIFAR1001, and Image Net20122 datasets. ... 1https://www.cs.toronto.edu/ kriz/cifar.html 2https://image-net.org/challenges/LSVRC/2012/ |
| Dataset Splits | No | The paper mentions using a "training set" to obtain the maximum activation value for neuron-wise normalization, but it does not specify any dataset splits (e.g., percentages or counts) for training, validation, or testing. |
| Hardware Specification | No | The paper discusses energy estimation using theoretical values for multiplication and addition operations (e.g., 4.6 pJ and 0.9 pJ from [Horowitz, 2014]), but it does not specify any concrete hardware details such as exact GPU/CPU models, processor types, or memory used for running its experiments. |
| Software Dependencies | No | The paper mentions general algorithms and techniques like SGD and Kaiming normal initialization but does not provide specific software dependencies with version numbers (e.g., PyTorch 1.x, TensorFlow 2.x, Python 3.x). |
| Experiment Setup | Yes | The initialization of ANN network parameters adopts Kaiming normal initialization [He et al., 2015], and the network training adopts SGD algorithm with 0.9 momentum followed by milestones learning rate decay. The L2 penalty with a value of 5e 4 is also added. |